Hi, I am trying to write code to compute covariance, computing the standard deviation of X and Y correctly (Just one point for computing the variance), and to correct answer using only one function. The data provided for the data set does contain nan so they need to be filtered out. Attached is a picture of the original prompt and the code I have tried/put together so far Write only one function that will take as argument 2 Numpy arrays (X and Y, which are of the same length, N) and then computes and outputs the followingquantity:= yi=N (Xi - X mean)(Y; – Ymcan)(oxoy)(N – 1)Cou(X, Y)PxY =ENoxoyThe quantity above is called the correlation and is defined as the covariance divided by the standard deviation in x and standard deviation in y. Thestandard deviation ox is the square root of the variance, given by:N - 1A similar formula applies to the standard deviation of Y.You need to use the following programming constructs:1. Functions2. Recursive loops3. Numpy arraysUse the file 'Wells.csv' for your data and compute the correlation between:1. RHOB and NPHI (Bulk density and Neutron porosity)2. ROP (Rate of Penetration) and WOB (Weight on Bit) In [1]: import pandas as pdIn [2]:M df = pd.read_csv('Wells.csv')In [3]: M dfOut[3]:Well DepthGRPEF1 PEF2DTROPWOB DownT Torque . DownPMudflowЕCDBSRT11. 2922.5 13.4058 8.7053NaN 77.18744.5008 4.301271.023.583382.2 2200.9165 1.41828.5 1.610011 2923.0 15.2468 6.4380NaN 75.50476.5108 4.954371.033.721382.5 1993.9286 1.41888.5 1.664821 2923.5 11.2243 6.2109NaN 75.56977.6733 7.043971.0 34.831382.7 1993.9286 1.41958.5 1.685611. 2924.011.7085 5.9728NaN 75.9891 10.2010 7.097772.035.166383.0 1993.9286 1.42048.5 1.463341 2924.5 16.3429 6.1139NaN 75.1929 12.8272 9.608972.034.892383.1 1993.92861.42068.5 1.5418......-..5594015 4083.5 59.7060NaN NaN 68.0602NaNNaNNaNNaNNaNNaNNaN NaN 1.75905594115 4084.0 58.4170NaNNaN 70.3944NaNNaNNaNNaNNaNNaNNaN NaN 1.65105594215 4084.5 57.4990NaNNaN 71.9931NaNNaNNaNNaN .NaNNaNNaN Nan 1.59705594315 4085.0 56.7850NaNNaN 72.7590NaNNaNNaNNaNNaNNaNNaN NaN 1.48205594415 4085.5 61.7220NaNNaN 72.8121NaNNaNNaNNaN ...NaNNaNNaN Nan 1.435055945 rows x 22 columnsIn [ ]:H df.shapeIn [ ]:I df.isnull().sum()In [ ]:I df.dropna(how='any', inplace-True)In [ ]:H df.shapeIn [ ]: M df [ 'ROP']In [ ]:I df = pd.read_csv('Wells.csv')In [ ]: N df['ROP']df['WOB']df['RHOB']W

In this dataset, we use simple function to find correlation between them.…

Answered: Use the file "Wells.csv for your data…

Use the file "Wells.csv for your data and compute the correlation between: 1. RHOB and NPHI (Bulk density and Neutron porosity) 2. ROP (Rate of Penetration) and WOB (Weight on Bit)

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question

Hi, I am trying to write code to compute covariance, computing the standard deviation of X and Y correctly (Just one point for computing the variance), and to correct answer using only one function. The data provided for the data set does contain nan so they need to be filtered out. Attached is a picture of the original prompt and the code I have tried/put together so far

Write only one function that will take as argument 2 Numpy arrays (X and Y, which are of the same length, N) and then computes and outputs the following
quantity:
= yi=N (Xi - X mean)(Y; – Ymcan)
(oxoy)(N – 1)
Cou(X, Y)
PxY =
EN
oxoy
The quantity above is called the correlation and is defined as the covariance divided by the standard deviation in x and standard deviation in y. The
standard deviation ox is the square root of the variance, given by:
N - 1
A similar formula applies to the standard deviation of Y.
You need to use the following programming constructs:
1. Functions
2. Recursive loops
3. Numpy arrays
Use the file 'Wells.csv' for your data and compute the correlation between:
1. RHOB and NPHI (Bulk density and Neutron porosity)
2. ROP (Rate of Penetration) and WOB (Weight on Bit)

In [1]: import pandas as pd
In [2]:
M df = pd.read_csv('Wells.csv')
In [3]: M df
Out[3]:
Well Depth
GR
PEF1 PEF2
DT
ROP
WOB DownT Torque . DownP
Mudflow
ЕCD
BS
RT
1
1. 2922.5 13.4058 8.7053
NaN 77.1874
4.5008 4.3012
71.0
23.583
382.2 2200.9165 1.4182
8.5 1.6100
1
1 2923.0 15.2468 6.4380
NaN 75.5047
6.5108 4.9543
71.0
33.721
382.5 1993.9286 1.4188
8.5 1.6648
2
1 2923.5 11.2243 6.2109
NaN 75.5697
7.6733 7.0439
71.0 34.831
382.7 1993.9286 1.4195
8.5 1.6856
1
1. 2924.0
11.7085 5.9728
NaN 75.9891 10.2010 7.0977
72.0
35.166
383.0 1993.9286 1.4204
8.5 1.4633
4
1 2924.5 16.3429 6.1139
NaN 75.1929 12.8272 9.6089
72.0
34.892
383.1 1993.9286
1.4206
8.5 1.5418
...
...
-..
55940
15 4083.5 59.7060
NaN NaN 68.0602
NaN
NaN
NaN
NaN
NaN
NaN
NaN NaN 1.7590
55941
15 4084.0 58.4170
NaN
NaN 70.3944
NaN
NaN
NaN
NaN
NaN
NaN
NaN NaN 1.6510
55942
15 4084.5 57.4990
NaN
NaN 71.9931
NaN
NaN
NaN
NaN .
NaN
NaN
NaN Nan 1.5970
55943
15 4085.0 56.7850
NaN
NaN 72.7590
NaN
NaN
NaN
NaN
NaN
NaN
NaN NaN 1.4820
55944
15 4085.5 61.7220
NaN
NaN 72.8121
NaN
NaN
NaN
NaN ...
NaN
NaN
NaN Nan 1.4350
55945 rows x 22 columns
In [ ]:
H df.shape
In [ ]:
I df.isnull().sum()
In [ ]:
I df.dropna(how='any', inplace-True)
In [ ]:
H df.shape
In [ ]: M df [ 'ROP']
In [ ]:
I df = pd.read_csv('Wells.csv')
In [ ]: N df['ROP']
df['WOB']
df['RHOB']
W

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